{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e06a4759",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "print(sns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45af97a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_tips = sns.load_dataset('tips')\n",
    "#df_tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ad35f96",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.load_dataset('tips')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5d18e2a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['anagrams',\n",
       " 'anscombe',\n",
       " 'attention',\n",
       " 'brain_networks',\n",
       " 'car_crashes',\n",
       " 'diamonds',\n",
       " 'dots',\n",
       " 'dowjones',\n",
       " 'exercise',\n",
       " 'flights',\n",
       " 'fmri',\n",
       " 'geyser',\n",
       " 'glue',\n",
       " 'healthexp',\n",
       " 'iris',\n",
       " 'mpg',\n",
       " 'penguins',\n",
       " 'planets',\n",
       " 'seaice',\n",
       " 'taxis',\n",
       " 'tips',\n",
       " 'titanic']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.get_dataset_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1ef7f09",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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